Quantitative precipitation estimate quality control

ABSTRACT

Systems and methods for improving the use of precipitation sensors, such as radar and rain gauges, are described herein. In an embodiment, an agricultural intelligence computer system receives one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations. The system additionally receives one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising predictions of precipitation at a plurality of lead times. The system identifies a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time. The system uses the plurality of forecast values to generate a probability of precipitation at each of the plurality of locations. The system determines that the probability of precipitation at a particular location is lower than a stored threshold value and, in response, store data identifying the particular location as having received no precipitation.

CROSS-REFERENCE TO RELATED APPLICATIONS, BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 119(e) of provisional application 62/784,309, filed Dec. 21, 2018, the entire contents of which is hereby incorporated by reference as if fully set forth herein.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright or rights whatsoever. © 2015-2018 The Climate Corporation.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is radar based precipitation monitoring. The technical field of the present disclosure further relates to creating agricultural recommendations and scripts for controlling agricultural implements to apply the recommendations.

BACKGROUND

The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

Precipitation measurements can be extremely important for agriculture. While accuracy in precipitation measurements can be useful for everyday life, agronomic fields can be strongly affected by the existence or lack of precipitation. For instance, crops require water to grow healthily. If a sensor determines that a field received precipitation when it actually did not, the crop may be underwatered, either by a field manager or an agricultural implement that performs activities based programmed scripts.

Where radar systems are advanced and highly populated, there tends to be few measurement errors. Conversely, locations with sparser networks of less advanced radar systems will often produce errors, even when modified using surrounding gauges. This effect can be more pronounced if the there is a lower density of gauges which provide field level verification of precipitation.

Radar systems are capable of generating two types of errors which are exacerbated with less advanced systems, sparser radar networks, or a lower density of gauges. The first type of error is the false identification of precipitation. False identification of precipitation can occur due to a small number of gauges in locations where precipitation is received being near an area where precipitation was not received. The second type of error is the overidentification of precipitation caused by artificially enhanced radar echoes.

Both types of errors can be catastrophic when used as a basis for agricultural decisions, implement action, and/or field level modeling. For instance, if precipitation is identified on a field where no precipitation occurred, or a much higher intensity of precipitation is identified on a field than actually occurred, a lesser amount of water may be added to the field by a field manager and/or agricultural implement. This can lead to a crop suffering from water stress which in turn can damage the crop or decrease the agronomic yield.

Thus, there is a need for a system which corrects errors in sensor-based measurements of precipitation on agricultural fields.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of sets of instructions in main memory when an example mobile application is loaded for execution.

FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using agronomic data provided by one or more data sources.

FIG. 4 is a block diagram that illustrates a computer system upon which an embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for data entry.

FIG. 7 depicts an example method for improving sensor measurements of precipitation.

FIG. 8 depicts an example for selecting forecasts from a plurality of forecast sets and generating a weighted estimate.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present disclosure. Embodiments are disclosed in sections according to the following outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW     -   2.2. APPLICATION PROGRAM OVERVIEW     -   2.3. DATA INGEST TO THE COMPUTER SYSTEM     -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING     -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. SENSOR-BASED PRECIPITATION MEASUREMENT CORRECTION

-   -   3.1. RECEIVED DATA     -   3.2. PROBABILITY OF PRECIPITATION     -   3.3. REMOVING PRECIPITATION VALUES     -   3.4. IDENTIFYING PERSISTENT CLUTTER     -   3.5. REDUCING PRECIPITATION VALUES

4. PRACTICAL APPLICATIONS

5. BENEFITS OF CERTAIN EMBODIMENTS

6. EXTENSIONS AND ALTERNATIVES

1. General Overview

Systems and methods for improving gridded quantitative precipitation estimates derived from sensors, such as rain gauges and radar, are described herein. In an embodiment, an agricultural intelligence computer system receives a gridded field of precipitation amounts for a 1-hour period and covering a large continuous region as compiled from combined sensor data. The system further receives a plurality of forecast sets covering the same region, each set comprising multiple hour forecasts, such that the forecast sets comprise overlapping forecasts with different lead times. The system identifies a plurality of forecast values for every location in the grid and date valid time with different lead times and uses the forecast values to generate a probability of precipitation as well as a maximum precipitation for the location and time. Using the probability of precipitation, the system is able to identify locations where the measurements showed precipitation but where the probability of precipitation is lower than a threshold value and store a value indicating that the location did not receive precipitation at the location and time. Using the maximum precipitation values, the system is able to reduce high precipitation values at identified persistent clutter locations to the maximum precipitation values when the measurements included values higher than the maximum precipitation values.

In an embodiment, a method comprises receiving one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations for a particular hour; receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising forecast precipitation at a plurality of lead times; identifying a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time; using the plurality of forecast values, generating a probability of precipitation at each of the plurality of locations, determining that the probability of precipitation at a particular location is lower than a stored threshold value and, in response, store data identifying the particular location as having received no precipitation.

In an embodiment, a method comprises receiving one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations; receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising estimates of precipitation at a plurality of lead times; identifying a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time; determining that a particular location of the plurality of locations has been identified as a persistent clutter location; using the plurality of forecast values, generating a maximum precipitation value for the particular location; determining that a particular digital data value representing precipitation intensity for the particular location is greater than the maximum precipitation value and, in response, replacing the particular digital data value with the maximum precipitation value.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.

Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (c) soil data (for example, type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) fertilizer data (for example, nutrient type (Nitrogen, Phosphorous, Potassium), application type, application date, amount, source, method), (f) chemical application data (for example, pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, application date, amount, source, method), (g) irrigation data (for example, application date, amount, source, method), (h) weather data (for example, precipitation, rainfall rate, predicted rainfall, water runoff rate region, temperature, wind, forecast, pressure, visibility, clouds, heat index, dew point, humidity, snow depth, air quality, sunrise, sunset), (i) imagery data (for example, imagery and light spectrum information from an agricultural apparatus sensor, camera, computer, smartphone, tablet, unmanned aerial vehicle, planes or satellite), (j) scouting observations (photos, videos, free form notes, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind velocity, relative humidity, dew point, black layer)), and (k) soil, seed, crop phenology, pest and disease reporting, and predictions sources and databases.

A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others. External data 110 may consist of the same type of information as field data 106. In some embodiments, the external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the agricultural intelligence computer system 130. For example, the agricultural intelligence computer system 130 may include a data server focused exclusively on a type of data that might otherwise be obtained from third party sources, such as weather data. In some embodiments, an external data server 108 may actually be incorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130. Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts that are used to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130. For instance, a controller area network (CAN) bus interface may be used to enable communications from the agricultural intelligence computer system 130 to the agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San Francisco, Calif., is used. Sensor data may consist of the same type of information as field data 106. In some embodiments, remote sensors 112 may not be fixed to an agricultural apparatus 111 but may be remotely located in the field and may communicate with network 109.

The apparatus 111 may comprise a cab computer 115 that is programmed with a cab application, which may comprise a version or variant of the mobile application for device 104 that is further described in other sections herein. In an embodiment, cab computer 115 comprises a compact computer, often a tablet-sized computer or smartphone, with a graphical screen display, such as a color display, that is mounted within an operator's cab of the apparatus 111. Cab computer 115 may implement some or all of the operations and functions that are described further herein for the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more data communication networks including local area networks, wide area networks, internetworks or internets, using any of wireline or wireless links, including terrestrial or satellite links. The network(s) may be implemented by any medium or mechanism that provides for the exchange of data between the various elements of FIG. 1. The various elements of FIG. 1 may also have direct (wired or wireless) communications links. The sensors 112, controller 114, external data server computer 108, and other elements of the system each comprise an interface compatible with the network(s) 109 and are programmed or configured to use standardized protocols for communication across the networks such as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS, and the like.

Agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from field manager computing device 104, external data 110 from external data server computer 108, and sensor data from remote sensor 112. Agricultural intelligence computer system 130 may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic such as FPGAs or ASICs, or any combination thereof to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts to application controller 114, in the manner described further in other sections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 is programmed with or comprises a communication layer 132, presentation layer 134, data management layer 140, hardware/virtualization layer 150, and model and field data repository 160. “Layer,” in this context, refers to any combination of electronic digital interface circuits, microcontrollers, firmware such as drivers, and/or computer programs or other software elements.

Communication layer 132 may be programmed or configured to perform input/output interfacing functions including sending requests to field manager computing device 104, external data server computer 108, and remote sensor 112 for field data, external data, and sensor data respectively. Communication layer 132 may be programmed or configured to send the received data to model and field data repository 160 to be stored as field data 106.

Presentation layer 134 may be programmed or configured to generate a graphical user interface (GUI) to be displayed on field manager computing device 104, cab computer 115 or other computers that are coupled to the system 130 through the network 109. The GUI may comprise controls for inputting data to be sent to agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.

Data management layer 140 may be programmed or configured to manage read operations and write operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. Examples of data management layer 140 include JDBC, SQL server interface code, and/or HADOOP interface code, among others. Repository 160 may comprise a database. As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, distributed databases, and any other structured collection of records or data that is stored in a computer system. Examples of RDBMS's include, but are not limited to including, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, any database may be used that enables the systems and methods described herein.

When field data 106 is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user 102 may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system 130) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers. In alternative embodiments, the user may specify identification data by accessing field identification data (provided as shape files or in a similar format) from the U.S. Department of Agriculture Farm Service Agency or other source via the user device and providing such field identification data to the agricultural intelligence computer system.

In an example embodiment, the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above, the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry. Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event. Events depicted at the top of the timeline may include Nitrogen, Planting, Practices, and Soil. To add a nitrogen application event, a user computer may provide input to select the nitrogen tab. The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creating one or more programs. “Program,” in this context, refers to a set of data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations. After a program has been created, it may be conceptually applied to one or more fields and references to the program may be stored in digital storage in association with data identifying the fields. Thus, instead of manually entering identical data relating to the same nitrogen applications for multiple different fields, a user computer may create a program that indicates a particular application of nitrogen and then apply the program to multiple different fields. For example, in the timeline view of FIG. 5, the top two timelines have the “Spring applied” program selected, which includes an application of 150 lbs N/ac in early April. The data manager may provide an interface for editing a program. In an embodiment, when a particular program is edited, each field that has selected the particular program is edited. For example, in FIG. 5, if the “Spring applied” program is edited to reduce the application of nitrogen to 130 lbs N/ac, the top two fields may be updated with a reduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has a program selected, the data manager removes the correspondence of the field to the selected program. For example, if a nitrogen application is added to the top field in FIG. 5, the interface may update to indicate that the “Spring applied” program is no longer being applied to the top field. While the nitrogen application in early April may remain, updates to the “Spring applied” program would not alter the April application of nitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for data entry. Using the display depicted in FIG. 6, a user can create and edit information for one or more fields. The data manager may include spreadsheets for inputting information with respect to Nitrogen, Planting, Practices, and Soil as depicted in FIG. 6. To edit a particular entry, a user computer may select the particular entry in the spreadsheet and update the values. For example, FIG. 6 depicts an in-progress update to a target yield value for the second field. Additionally, a user computer may select one or more fields in order to apply one or more programs. In response to receiving a selection of a program for a particular field, the data manager may automatically complete the entries for the particular field based on the selected program. As with the timeline view, the data manager may update the entries for each field associated with a particular program in response to receiving an update to the program. Additionally, the data manager may remove the correspondence of the selected program to the field in response to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field data repository 160. Model data comprises data models created for one or more fields. For example, a crop model may include a digitally constructed model of the development of a crop on the one or more fields. “Model,” in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored or calculated output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. Persons of skill in the field find it convenient to express models using mathematical equations, but that form of expression does not confine the models disclosed herein to abstract concepts; instead, each model herein has a practical application in a computer in the form of stored executable instructions and data that implement the model using the computer. The model may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.

In an embodiment, each of forecast ensemble generation instructions 136, precipitation removal instructions 137, and precipitation reduction instructions 138 comprises a set of one or more pages of main memory, such as RAM, in the agricultural intelligence computer system 130 into which executable instructions have been loaded and which when executed cause the agricultural intelligence computer system to perform the functions or operations that are described herein with reference to those modules. For example, the forecast ensemble generation instructions 136 may comprise a set of pages in RAM that contain instructions which when executed cause performing the ensemble generation functions that are described herein. The instructions may be in machine executable code in the instruction set of a CPU and may have been compiled based upon source code written in JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts in JAVASCRIPT, other scripting languages and other programming source text. The term “pages” is intended to refer broadly to any region within main memory and the specific terminology used in a system may vary depending on the memory architecture or processor architecture. In another embodiment, each of forecast ensemble generation instructions 136, precipitation removal instructions 137, and precipitation reduction instructions 138 also may represent one or more files or projects of source code that are digitally stored in a mass storage device such as non-volatile RAM or disk storage, in the agricultural intelligence computer system 130 or a separate repository system, which when compiled or interpreted cause generating executable instructions which when executed cause the agricultural intelligence computer system to perform the functions or operations that are described herein with reference to those modules. In other words, the drawing figure may represent the manner in which programmers or software developers organize and arrange source code for later compilation into an executable, or interpretation into bytecode or the equivalent, for execution by the agricultural intelligence computer system 130.

In an embodiment, forecast ensemble generation instructions 136 comprise computer-readable instructions which, when executed by one or more processors, cause the agricultural intelligence computer system to generate an ensemble of forecast for a particular location and time based on forecasts with different lead times from a plurality of forecast sets. In an embodiment, precipitation removal instructions 137 comprise computer-readable instructions which, when executed by one or more processors, cause the agricultural intelligence computer system to identify locations with a low probability of precipitation and remove measured precipitation values for the identified locations. In an embodiment, precipitation reduction instructions 138 comprise computer readable instructions which, when executed by one or more processors, cause the agricultural intelligence computer system to identify maximum expected precipitation values for a plurality of locations and reduce measured precipitation values for locations where the measured precipitation values exceed the maximum expected precipitation values.

Hardware/virtualization layer 150 comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system such as volatile or non-volatile memory, non-volatile storage such as disk, and I/O devices or interfaces as illustrated and described, for example, in connection with FIG. 4. The layer 150 also may comprise programmed instructions that are configured to support virtualization, containerization, or other technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limited number of instances of certain functional elements. However, in other embodiments, there may be any number of such elements. For example, embodiments may use thousands or millions of different mobile computing devices 104 associated with different users. Further, the system 130 and/or external data server computer 108 may be implemented using two or more processors, cores, clusters, or instances of physical machines or virtual machines, configured in a discrete location or co-located with other elements in a datacenter, shared computing facility or cloud computing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described herein using one or more computer programs or other software elements that are loaded into and executed using one or more general-purpose computers will cause the general-purpose computers to be configured as a particular machine or as a computer that is specially adapted to perform the functions described herein. Further, each of the flow diagrams that are described further herein may serve, alone or in combination with the descriptions of processes and functions in prose herein, as algorithms, plans or directions that may be used to program a computer or logic to implement the functions that are described. In other words, all the prose text herein, and all the drawing figures, together are intended to provide disclosure of algorithms, plans or directions that are sufficient to permit a skilled person to program a computer to perform the functions that are described herein, in combination with the skill and knowledge of such a person given the level of skill that is appropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligence computer system 130 using field manager computing device 104 configured with an operating system and one or more application programs or apps; the field manager computing device 104 also may interoperate with the agricultural intelligence computer system independently and automatically under program control or logical control and direct user interaction is not always required. Field manager computing device 104 broadly represents one or more of a smart phone, PDA, tablet computing device, laptop computer, desktop computer, workstation, or any other computing device capable of transmitting and receiving information and performing the functions described herein. Field manager computing device 104 may communicate via a network using a mobile application stored on field manager computing device 104, and in some embodiments, the device may be coupled using a cable 113 or connector to the sensor 112 and/or controller 114. A particular user 102 may own, operate or possess and use, in connection with system 130, more than one field manager computing device 104 at a time.

The mobile application may provide client-side functionality, via the network to one or more mobile computing devices. In an example embodiment, field manager computing device 104 may access the mobile application via a web browser or a local client application or app. Field manager computing device 104 may transmit data to, and receive data from, one or more front-end servers, using web-based protocols or formats such as HTTP, XML, and/or JSON, or app-specific protocols. In an example embodiment, the data may take the form of requests and user information input, such as field data, into the mobile computing device. In some embodiments, the mobile application interacts with location tracking hardware and software on field manager computing device 104 which determines the location of field manager computing device 104 using standard tracking techniques such as multilateration of radio signals, the global positioning system (GPS), WiFi positioning systems, or other methods of mobile positioning. In some cases, location data or other data associated with the device 104, user 102, and/or user account(s) may be obtained by queries to an operating system of the device or by requesting an app on the device to obtain data from the operating system.

In an embodiment, field manager computing device 104 sends field data 106 to agricultural intelligence computer system 130 comprising or including, but not limited to, data values representing one or more of: a geographical location of the one or more fields, tillage information for the one or more fields, crops planted in the one or more fields, and soil data extracted from the one or more fields. Field manager computing device 104 may send field data 106 in response to user input from user 102 specifying the data values for the one or more fields. Additionally, field manager computing device 104 may automatically send field data 106 when one or more of the data values becomes available to field manager computing device 104. For example, field manager computing device 104 may be communicatively coupled to remote sensor 112 and/or application controller 114 which include an irrigation sensor and/or irrigation controller. In response to receiving data indicating that application controller 114 released water onto the one or more fields, field manager computing device 104 may send field data 106 to agricultural intelligence computer system 130 indicating that water was released on the one or more fields. Field data 106 identified in this disclosure may be input and communicated using electronic digital data that is communicated between computing devices using parameterized URLs over HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW, commercially available from The Climate Corporation, San Francisco, Calif. The CLIMATE FIELDVIEW application, or other applications, may be modified, extended, or adapted to include features, functions, and programming that have not been disclosed earlier than the filing date of this disclosure. In one embodiment, the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare. The combinations and comparisons may be performed in real time and are based upon scientific models that provide potential scenarios to permit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of sets of instructions in main memory when an example mobile application is loaded for execution. In FIG. 2, each named element represents a region of one or more pages of RAM or other main memory, or one or more blocks of disk storage or other non-volatile storage, and the programmed instructions within those regions. In one embodiment, in view (a), a mobile computer application 200 comprises account-fields-data ingestion-sharing instructions 202, overview and alert instructions 204, digital map book instructions 206, seeds and planting instructions 208, nitrogen instructions 210, weather instructions 212, field health instructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account, fields, data ingestion, sharing instructions 202 which are programmed to receive, translate, and ingest field data from third party systems via manual upload or APIs. Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others. Data formats may include shape files, native data formats of third parties, and/or farm management information system (FMIS) exports, among others. Receiving data may occur via manual upload, e-mail with attachment, external APIs that push data to the mobile application, or instructions that call APIs of external systems to pull data into the mobile application. In one embodiment, mobile computer application 200 comprises a data inbox. In response to receiving a selection of the data inbox, the mobile computer application 200 may display a graphical user interface for manually uploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field map data layers stored in device memory and are programmed with data visualization tools and geospatial field notes. This provides growers with convenient information close at hand for reference, logging and visual insights into field performance. In one embodiment, overview and alert instructions 204 are programmed to provide an operation-wide view of what is important to the grower, and timely recommendations to take action or focus on particular issues. This permits the grower to focus time on what needs attention, to save time and preserve yield throughout the season. In one embodiment, seeds and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population.

In one embodiment, script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. The interface enables growers to create scripts for field implements, such as nutrient applications, planting, and irrigation. For example, a planting script interface may comprise tools for identifying a type of seed for planting. Upon receiving a selection of the seed type, mobile computer application 200 may display one or more fields broken into management zones, such as the field map data layers created as part of digital map book instructions 206. In one embodiment, the management zones comprise soil zones along with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. Mobile computer application 200 may also display tools for editing or creating such, such as graphical tools for drawing management zones, such as soil zones, over a map of one or more fields. Planting procedures may be applied to all management zones or different planting procedures may be applied to different subsets of management zones. When a script is created, mobile computer application 200 may make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally, and/or alternatively, a script may be sent directly to cab computer 115 from mobile computer application 200 and/or uploaded to one or more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the season. Example programmed functions include displaying images such as SSURGO images to enable drawing of fertilizer application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as millimeters or smaller depending on sensor proximity and resolution); upload of existing grower-defined zones; providing a graph of plant nutrient availability and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others. “Mass data entry,” in this context, may mean entering data once and then applying the same data to multiple fields and/or zones that have been defined in the system; example data may include nitrogen application data that is the same for many fields and/or zones of the same grower, but such mass data entry applies to the entry of any type of field data into the mobile computer application 200. For example, nitrogen instructions 210 may be programmed to accept definitions of nitrogen application and practices programs and to accept user input specifying to apply those programs across multiple fields. “Nitrogen application programs,” in this context, refers to stored, named sets of data that associates: a name, color code or other identifier, one or more dates of application, types of material or product for each of the dates and amounts, method of application or incorporation such as injected or broadcast, and/or amounts or rates of application for each of the dates, crop or hybrid that is the subject of the application, among others. “Nitrogen practices programs,” in this context, refer to stored, named sets of data that associates: a practices name; a previous crop; a tillage system; a date of primarily tillage; one or more previous tillage systems that were used; one or more indicators of application type, such as manure, that were used. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen graph, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall. In one embodiment, a nitrogen graph comprises a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crop is planted in the field, the field size, the field location, and a graphic representation of the field perimeter; in each row, a timeline by month with graphic indicators specifying each nitrogen application and amount at points correlated to month names; and numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.

In one embodiment, the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen map, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall. The nitrogen map may display projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted for different times in the past and the future (such as daily, weekly, monthly or yearly) using numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. In other embodiments, similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to provide field-specific recent weather data and forecasted weather information. This enables growers to save time and have an efficient integrated display with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include cloud checking, to identify possible clouds or cloud shadows; determining nitrogen indices based on field images; graphical visualization of scouting layers, including, for example, those related to field health, and viewing and/or sharing of scouting notes; and/or downloading satellite images from multiple sources and prioritizing the images for the grower, among others.

In one embodiment, performance instructions 216 are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others. Programmed reports and analysis may include yield variability analysis, treatment effect estimation, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.

Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance. For example, the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers. Further, the mobile application as configured for tablet computers or smartphones may provide a full app experience or a cab app experience that is suitable for the display and processing capabilities of cab computer 115. For example, referring now to view (b) of FIG. 2, in one embodiment a cab computer application 220 may comprise maps-cab instructions 222, remote view instructions 224, data collect and transfer instructions 226, machine alerts instructions 228, script transfer instructions 230, and scouting-cab instructions 232. The code base for the instructions of view (b) may be the same as for view (a) and executables implementing the code may be programmed to detect the type of platform on which they are executing and to expose, through a graphical user interface, only those functions that are appropriate to a cab platform or full platform. This approach enables the system to recognize the distinctly different user experience that is appropriate for an in-cab environment and the different technology environment of the cab. The maps-cab instructions 222 may be programmed to provide map views of fields, farms or regions that are useful in directing machine operation. The remote view instructions 224 may be programmed to turn on, manage, and provide views of machine activity in real-time or near real-time to other computing devices connected to the system 130 via wireless networks, wired connectors or adapters, and the like. The data collect and transfer instructions 226 may be programmed to turn on, manage, and provide transfer of data collected at sensors and controllers to the system 130 via wireless networks, wired connectors or adapters, and the like. The machine alerts instructions 228 may be programmed to detect issues with operations of the machine or tools that are associated with the cab and generate operator alerts. The script transfer instructions 230 may be configured to transfer in scripts of instructions that are configured to direct machine operations or the collection of data. The scouting-cab instructions 232 may be programmed to display location-based alerts and information received from the system 130 based on the location of the field manager computing device 104, agricultural apparatus 111, or sensors 112 in the field and ingest, manage, and provide transfer of location-based scouting observations to the system 130 based on the location of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data 110, including soil data representing soil composition for the one or more fields and weather data representing temperature and precipitation on the one or more fields. The weather data may include past and present weather data as well as forecasts for future weather data. In an embodiment, external data server computer 108 comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data while a second server may include weather data. Additionally, soil composition data may be stored in multiple servers. For example, one server may store data representing percentage of sand, silt, and clay in the soil while a second server may store data representing percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement. For example, an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve. Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed “manual data ingest” as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130. As an example, the CLIMATE FIELDVIEW application, commercially available from The Climate Corporation, San Francisco, Calif., may be operated to export data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatus components and obtain planting data, including signals from seed sensors via a signal harness that comprises a CAN backbone and point-to-point connections for registration and/or diagnostics. Seed monitor systems can be programmed or configured to display seed spacing, population and other information to the user via the cab computer 115 or other devices within the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243 and US Pat. Pub. 20150094916, and the present disclosure assumes knowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvester apparatus that send yield measurement data to the cab computer 115 or other devices within the system 130. Yield monitor systems may utilize one or more remote sensors 112 to obtain grain moisture measurements in a combine or other harvester and transmit these measurements to the user via the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with any moving vehicle or apparatus of the type described elsewhere herein include kinematic sensors and position sensors. Kinematic sensors may comprise any of speed sensors such as radar or wheel speed sensors, accelerometers, or gyros. Position sensors may comprise GPS receivers or transceivers, or WiFi-based position or mapping apps that are programmed to determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area counters or distance counters that interact with GPS or radar signals, PTO (power take-off) speed sensors, tractor hydraulics sensors configured to detect hydraulics parameters such as pressure or flow, and/or and hydraulic pump speed, wheel speed sensors or wheel slippage sensors. In an embodiment, examples of controllers 114 that may be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers; hydraulic pump speed controllers; speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seed planting equipment such as planters, drills, or air seeders include seed sensors, which may be optical, electromagnetic, or impact sensors; downforce sensors such as load pins, load cells, pressure sensors; soil property sensors such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors; component operating criteria sensors such as planting depth sensors, downforce cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed conveyor system speed sensors, or vacuum level sensors; or pesticide application sensors such as optical or other electromagnetic sensors, or impact sensors. In an embodiment, examples of controllers 114 that may be used with such seed planting equipment include: toolbar fold controllers, such as controllers for valves associated with hydraulic cylinders; downforce controllers, such as controllers for valves associated with pneumatic cylinders, airbags, or hydraulic cylinders, and programmed for applying downforce to individual row units or an entire planter frame; planting depth controllers, such as linear actuators; metering controllers, such as electric seed meter drive motors, hydraulic seed meter drive motors, or swath control clutches; hybrid selection controllers, such as seed meter drive motors, or other actuators programmed for selectively allowing or preventing seed or an air-seed mixture from delivering seed to or from seed meters or central bulk hoppers; metering controllers, such as electric seed meter drive motors, or hydraulic seed meter drive motors; seed conveyor system controllers, such as controllers for a belt seed delivery conveyor motor; marker controllers, such as a controller for a pneumatic or hydraulic actuator; or pesticide application rate controllers, such as metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillage equipment include position sensors for tools such as shanks or discs; tool position sensors for such tools that are configured to detect depth, gang angle, or lateral spacing; downforce sensors; or draft force sensors. In an embodiment, examples of controllers 114 that may be used with tillage equipment include downforce controllers or tool position controllers, such as controllers configured to control tool depth, gang angle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relation to apparatus for applying fertilizer, insecticide, fungicide and the like, such as on-planter starter fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating which spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors such as accelerometers disposed on sprayer booms. In an embodiment, examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.

In an embodiment, examples of sensors 112 that may be used with harvesters include yield monitors, such as impact plate strain gauges or position sensors, capacitive flow sensors, load sensors, weight sensors, or torque sensors associated with elevators or augers, or optical or other electromagnetic grain height sensors; grain moisture sensors, such as capacitive sensors; grain loss sensors, including impact, optical, or capacitive sensors; header operating criteria sensors such as header height, header type, deck plate gap, feeder speed, and reel speed sensors; separator operating criteria sensors, such as concave clearance, rotor speed, shoe clearance, or chaffer clearance sensors; auger sensors for position, operation, or speed; or engine speed sensors. In an embodiment, examples of controllers 114 that may be used with harvesters include header operating criteria controllers for elements such as header height, header type, deck plate gap, feeder speed, or reel speed; separator operating criteria controllers for features such as concave clearance, rotor speed, shoe clearance, or chaffer clearance; or controllers for auger position, operation, or speed.

In an embodiment, examples of sensors 112 that may be used with grain carts include weight sensors, or sensors for auger position, operation, or speed. In an embodiment, examples of controllers 114 that may be used with grain carts include controllers for auger position, operation, or speed.

In an embodiment, examples of sensors 112 and controllers 114 may be installed in unmanned aerial vehicle (UAV) apparatus or “drones.” Such sensors may include cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near-infrared (NIR), and the like; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other airspeed or wind velocity sensors; battery life sensors; or radar emitters and reflected radar energy detection apparatus; other electromagnetic radiation emitters and reflected electromagnetic radiation detection apparatus. Such controllers may include guidance or motor control apparatus, control surface controllers, camera controllers, or controllers programmed to turn on, operate, obtain data from, manage and configure any of the foregoing sensors. Examples are disclosed in U.S. patent application Ser. No. 14/831,165 and the present disclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil. For example, the apparatus disclosed in U.S. Pat. No. 8,767,194 and U.S. Pat. No. 8,712,148 may be used, and the present disclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weather devices for monitoring weather conditions of fields. For example, the apparatus disclosed in U.S. Provisional Application No. 62/154,207, filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160, filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060, filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852, filed on Sep. 18, 2015, may be used, and the present disclosure assumes knowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations and other crop management recommendations. The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data. The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location or an estimate of nitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. FIG. 3 may serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data including measured outliers that could adversely affect received field data values. Embodiments of agronomic data preprocessing may include, but are not limited to, removing data values commonly associated with outlier data values, specific measured data points that are known to unnecessarily skew other data values, data smoothing, aggregation, or sampling techniques used to remove or reduce additive or multiplicative effects from noise, and other filtering or data derivation techniques used to provide clear distinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the preprocessed field data in order to identify datasets useful for initial agronomic model generation. The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 is configured or programmed to implement field dataset evaluation. In an embodiment, a specific field dataset is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models may be compared and/or validated using one or more comparison techniques, such as, but not limited to, root mean square error with leave-one-out cross validation (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross validate agronomic models by comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed. In an embodiment, the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic model creation based upon the cross validated agronomic datasets. In an embodiment, agronomic model creation may implement multivariate regression techniques to create preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 is configured or programmed to store the preconfigured agronomic data models for future field data evaluation.

2.5. Implementation Example-Hardware Overview

According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computer system 400 upon which an embodiment of the invention may be implemented. Computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor 404 coupled with bus 402 for processing information. Hardware processor 404 may be, for example, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 for communicating information and command selections to processor 404. Another type of user input device is cursor control 416, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.

Computer system 400 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 400 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406. Such instructions may be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor 404 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage device 410. Volatile media includes dynamic memory, such as main memory 406. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 402. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications.

Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 404 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 400 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infrared signal and appropriate circuitry can place the data on bus 402. Bus 402 carries the data to main memory 406, from which processor 404 retrieves and executes the instructions. The instructions received by main memory 406 may optionally be stored on storage device 410 either before or after execution by processor 404.

Computer system 400 also includes a communication interface 418 coupled to bus 402. Communication interface 418 provides a two-way data communication coupling to a network link 420 that is connected to a local network 422. For example, communication interface 418 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 418 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 418 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network link 420 typically provides data communication through one or more networks to other data devices. For example, network link 420 may provide a connection through local network 422 to a host computer 424 or to data equipment operated by an Internet Service Provider (ISP) 426. ISP 426 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 428. Local network 422 and Internet 428 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 420 and through communication interface 418, which carry the digital data to and from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, including program code, through the network(s), network link 420 and communication interface 418. In the Internet example, a server 430 might transmit a requested code for an application program through Internet 428, ISP 426, local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received, and/or stored in storage device 410, or other non-volatile storage for later execution.

3. Sensor-Based Precipitation Measurement Correction

FIG. 7 depicts an example method for improving sensor measurements of precipitation. In an embodiment, the process of FIG. 7 may be implemented in executable instructions by the agricultural intelligence computer system.

3.1. Received Data

At step 702, one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations is received. Agricultural intelligence computer system 130 may obtain the radar-based precipitation estimates by initially receiving radar precipitation estimates from external data server computer 108. Additionally and/or alternatively, agricultural intelligence computer system 130 may initially receive radar reflectivity measurements from the external data server computer 108 and compute the radar-based precipitation estimates from the radar reflectivity measurements. In an embodiment, external data server computer 108 comprises a plurality of server computers owned or operated by different entities. For example, agricultural intelligence computer system 130 may be communicatively coupled to one or more radar server computers operated by a first entity and one or more radar server computers operated by a second entity.

The one or more radar server computers may be communicatively coupled to a radar device which emits a polarized signal towards the plurality of locations and receives scattered energy. In some embodiments, agricultural intelligence computer system 130 receives reflectivity data, comprising a location of the radar device, an amount of energy emitted from the radar device, a direction of the energy emission, an amount of time between the emission and the receipt of the scattered energy, and an amount of scattered energy received. From the reflectivity data, agricultural intelligence computer system 130 may compute the location of the precipitation and the magnitude of the precipitation.

In other embodiments, one or more initial computations may be performed in advance, such as by the one or more radar server computers, and agricultural intelligence computer system 130 may receive location and/or precipitation magnitude estimates from the one or more radar server computers. For example, agricultural intelligence computer system 130 may send a digital request or message to the one or more radar server computers to retrieve radar measurements or precipitation estimates for a plurality of locations across a particular region. The request or message may specify the locations of interest by latitude-longitude values or other identification values.

In response, the one or more radar computer servers may compute the location of precipitation for each reflectivity measurement and may identify energy measurements that are associated with the plurality of locations based on the nature of reflectivity data or the energy emission and reception methods specified above. The one or more radar computer servers may send the reflectivity measurements associated with the plurality of locations to agricultural intelligence computer system 130 in one or more response messages. Additionally and/or alternatively, the one or more radar computer servers may compute estimates for the amount of precipitation at the plurality of locations and send the computed estimates to agricultural intelligence computer system 130.

Agricultural intelligence computer system 130 may be programmed or configured to receive radar data from multiple different sources. Agricultural intelligence computer system 130 may use the radar data received from different sources to strengthen the computation of precipitation intensities and the determination of the errors in the precipitation intensities. For example, agricultural intelligence computer system 130 may receive quantitative precipitation estimate data of the Multi-Radar Multi-Sensor system run at the National Centers for Environmental Prediction (NCEP). In an embodiment, agricultural intelligence computer system 130 receives precipitation data that has been generated using radar and gauge measurements. For example, the agricultural intelligence computer system 130 may receive gauge corrected MRMS estimates from the NCEP.

In an embodiment, agricultural intelligence computer system 130 receives radar reflectivity measurements and/or radar-based precipitation estimates at a plurality of separate times (“snapshots”) across a plurality of locations. For example, for a plurality of locations, agricultural intelligence computer system 130 may receive a first plurality of radar reflectivity measurements and/or radar-based precipitation estimates corresponding to a first time and a second plurality of radar reflectivity measurements and/or radar-based precipitation estimates corresponding to a second time. Thus, each plurality of precipitation measurements corresponds to a different time. The different times may be evenly spaced apart. For example, radar systems may take radar reflectivity measurements at intervals, such as four-minute intervals, throughout a given day. Intervals may be regular or irregular.

In an embodiment, agricultural intelligence computer system 130 generates precipitation rate estimate maps for each time of a plurality of times. For example, agricultural intelligence computer system 130 may store each precipitation estimate with data identifying a particular time and location, such as latitude and longitude. Agricultural intelligence computer system 130 may identify each precipitation estimate associated with a first time and generate a first map of precipitation estimates for the first time. Agricultural intelligence computer system 130 may then identify each precipitation estimate associated with a second time and generate a second map of precipitation estimates for the second time. Additionally or alternatively, agricultural intelligence computer system may receive maps of precipitation estimates from an external data source. A “map,” in this context, refers to digitally stored data that can be interpreted or used as the basis of generating a graphical image of a geographic area on a computer display.

At step 704, one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts are received, each of which comprise estimates of precipitation at a plurality of lead times. For example, the agricultural intelligence computer system may generate forecasts based, at least in part, on prior weather data and/or request and receive forecasts from an external data source. Each set of forecasts may include a plurality of forecasts for different lead times. For example, a five-hour precipitation forecast set with a time of 9:00 am may include precipitation forecasts at 10:00 am, 11:00 am, 12:00 am, 1:00 pm, and 2:00 pm. The lead time, as used herein, refers to a difference in time between the time of the forecast set and the valid time of an individual forecast in the set. For example, in the above set, the lead time for the 10:00 am forecast may be one hour while the lead time for the 2:00 pm forecast may be five hours.

Multiple sets of forecasts from the same data source may include overlapping times. For example, if a source produces a five-hour forecast every hour, a forecast for 10 am may be available from the 9 am forecast with a one-hour lead time, the 8 am forecast with a two-hour lead time, and so on. The agricultural intelligence computer intelligence computer system may receive a plurality of sets of forecasts comprising a plurality of overlapping forecasts for the same time and location, but at different lead times.

In an embodiment, the agricultural intelligence computer system receives forecast sets from a plurality of sources, such as the 3-km High Resolution Rapid Refresh (HRRR) model and the 13-km Rapid Refresh (RAP) model prepared by the NCEP. The system may map the forecasts from different sources to a common grid, such as through bilinear interpolation of the forecasted precipitation values to different locations of the grid.

3.2. Probability of Precipitation

At step 706, the system identifies a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time. For example, the system may identify forecasts from different forecast sets that correspond to the same location and time. Thus, forecasts for 10:00 am may be extracted from various forecast sets that include a 10:00 am forecast. As an example, the system may use data from a source that includes forecast data at 1-hour increments over an extended forecast period and extract forecasts for a specific time using forecast sets where the forecast for the specific time has a lead time of two hours to eleven hours, thereby creating a set of ten forecasts for a single set.

At step 708, a probability of precipitation at each of the plurality of locations is computed. The probability of precipitation may be computed from forecasts weighted by lead time. FIG. 8 depicts an example for selecting forecasts from a plurality of forecast sets and generating a weighted estimate. In FIG. 8, forecasts 800 are 10-hour forecasts generated every hour. For instance, the first forecast F1 was generated at 8:00 and includes forecasts up through 6:00. To generate a forecast for 3:00, the forecast for 3:00 is selected from each set of forecasts 800. Each of forecasts 800 has a different lead time for the forecast.

Weighted estimate 820 comprises a probability of precipitation and is generated using the number of selected forecasts weighted by their lead time with higher weight being given to the shortest lead time. In this example, forecast F4 with a lead time of four hours is weighted higher than forecast F3 with a lead time of five hours. As an example, the probability of precipitation may be computed as weighted estimate 820 using the following equation:

$P = \frac{\Sigma \; {w_{i}\left( {F > 0} \right)}}{\Sigma \; {w_{i}\left( {F \geq 0} \right)}}$

where (F>0) is 1.0 for all values where the prescription for the forecast is greater than zero, (F>0) is 1.0 where the prescription for the forecast is not missing, and the weight w_(i) for each forecast is computed as a linear function of the lead time that may be tuned and optimized in accordance with the forecasts' error characteristics. The probability of precipitation value may be computed using forecasts with lead times of two to eleven hours.

In an embodiment, the probability of precipitation is computed using forecasts from two different sources. For example, the system may use the HRRR model and the RAP model prepared by the NCEP to generate the probability of precipitation. The system may further weight the forecasts based on the source of the forecast. For example, as the RAP model comprises a 13-km forecast while the HRRR model comprises a 3-km. Thus, in order to capture the higher accuracy of the HRRR model, the system may weigh the HRRR model higher than the RAP model. As an example, the probability of precipitation using two sources of forecasts may be computed as:

$P = \frac{{\Sigma \; {w_{i}\left( {F_{1} > 0} \right)}} + {{w_{i}\left( {F_{2} > 0} \right)}k}}{{\Sigma \; {w_{i}\left( {F_{1} \geq 0} \right)}} + {{w_{i}\left( {F_{2} \geq 0} \right)}k}}$

where F₁ and F₂ are forecasts from the two models, w_(i) is a weight for each forecast calculated as described above, and k is a relative weight for the forecast type. The value of k may change or be adjusted based on a relative weight or accuracy of a particular model in relation to another model. For example, k may be initially set to 0.2 to reflect a lower skill of the second model. The relative weight may be determined empirically based on the two models used.

In an embodiment, the agricultural intelligence computer system generates probabilities of precipitation for each location of a plurality of locations, such as each grid point on a grid with spacing of 1-km between grid points. Thus, the agricultural intelligence computer system may generate a map of grid points for a particular time where each grid point comprises a probability of precipitation at the particular time.

In an embodiment, in order to fill in gaps and/or errors, the agricultural intelligence computer system further performs a smoothing step to increase the probability of precipitation at locations where the probability of precipitation is low despite being near locations where the probability of precipitation is high. As an example, the agricultural intelligence computer system may use a simple average smoother with a radius of 15 grid points to generate smoothed grid points for each of the grid locations. The simple average smoother may be any spatial smoothing technique which utilizes data from surrounding locations to compute a value for a particular location.

In an embodiment, after performing the smoothing step, the agricultural intelligence computer system may identify each location where the smoothing step reduced the probability of precipitation. For each identified location, the agricultural intelligence computer system may replace the smoothed probability of precipitation with the original probability of precipitation. Thus, the agricultural intelligence computer system is able to use spatial smoothing techniques to increase the probabilities of precipitation at locations that are near high probability locations without reducing the computed probabilities at any of the smoothed locations.

3.3. Removing Precipitation Values

At step 710, the system determines that the probability of precipitation at a particular location is lower than a stored threshold and, in response, stores data identifying the particular location as having received no precipitation. For example, the agricultural intelligence computer system may determine that locations with lower that a threshold probability of having received precipitation likely did not receive precipitation. Thus, the agricultural intelligence computer system may set the measured precipitation estimates to zero where a computation of the probabilities of precipitation were lower than a predetermined threshold value to reduce instances of overidentification of precipitation where none actually occurred based on errors in the radar-based precipitation measurements.

In an embodiment, the threshold value may be predetermined generally. For example, the probability of precipitation threshold may be set to 10% such that any location with a probability of precipitation lower than 10% is assumed to not have received precipitation. Additionally or alternatively, the threshold value may be computed for a specific period of time. For example, a probability of precipitation threshold may be computed for a month based on data from a previous month. As another example, the probability of precipitation threshold may be computed each day based on data from the last 30 days.

The agricultural intelligence computer system may use statistical modeling techniques to select the probability of precipitation threshold based on the state of a particular statistical model. For example, the agricultural intelligence computer system may receive verification data indicating, for each of a plurality of locations, whether the location received precipitation as well as the intensity of precipitation. The agricultural intelligence computer system may compute a threshold value such that would maximize the number of instances of removals of false positives of precipitation while minimizing the number of instances of removals of correct positives.

As used herein, the false positive refers to an instance where the estimated precipitation amount was greater than zero, but no precipitation occurred in the location as identified through the verification data. A correct positive is thus an instance where the estimated precipitation amount was greater than zero and precipitation occurred in the location as identified through the verification data. The threshold value may thus be selected to minimize the number of removals of correct positives while maximizing removals of false positives over the prior 30 days. An example equation is as follows:

$\frac{\Sigma \; {w\left( {P_{c} \geq T} \right)}}{\Sigma \left( {P_{f} < T} \right)}$

where P_(c) is the probability of precipitation for each location where precipitation actually occurred, P_(f) is the probability of precipitation for each location where precipitation did not actually occur, T is the threshold value, and w is a weight value which is selected to increase or decrease the value of not misidentifying actual positives as false positives.

By computing a new threshold value for a period of time, the system is able to account for seasonal variations in errors from the precipitation sensors. For instance, the sensors may generate false positives at a higher rate in Fall. Thus, the system may use the equation above to adaptively increase the threshold value such that the false positives are removed.

3.4. Identifying Persistent Clutter

In an embodiment, the agricultural intelligence computer system uses prior measurements and estimates to identify locations as persistent clutter locations. The agricultural intelligence computer system may identify persistent clutter locations as part of a method of reducing the estimated precipitation amount at locations where the frequency of occurrence of estimated precipitation tends to be higher than the actual frequency of precipitation occurrence. As used herein, a persistent clutter location is a location that has been identified by the agricultural intelligence computer system as often comprising a precipitation amount greater than zero received from sensors that is completely false, thus resulting in a frequency of precipitation higher than an expected precipitation frequency, and when not completely false, may likely be in excess of an expected amount.

Referring again to FIG. 7, at step 712, the system determines that a particular location of the plurality of locations has been identified as a persistent clutter location. To identify the persistent clutter locations, the agricultural intelligence computer system may first compute an expected precipitation frequency for each of a plurality of locations based on forecasts from a plurality of a single lead time taken from a long series of historical forecast sets. The expected precipitation frequency of precipitation for a particular location may be computed as an average occurrence of precipitation greater than zero over the entire period covered by the sequence of forecasts.

In an embodiment, multiple models may be used to compute the expected precipitation frequency value in order to ensure complete spatial coverage of results. For example, the agricultural intelligence computer system may compute the expected precipitation frequency value using forecast values from both the HRRR model and the RAP model. If one model tends to display higher frequency values than the other, the models may be scaled down or scaled up. For example, if the RAP model generally comprises higher frequency values, the values may be scaled down by a set value, such as 0.7. The value may be determined by comparing precipitation frequency values of the two models to determine the average scaling between the models.

The agricultural intelligence computer system may compute expected precipitation frequency over a long period of historical forecasts, such as the 5-hour lead time from each forecast produced hourly across a thirty-day period. The agricultural intelligence computer system may then identify locations where the expected precipitation frequency exceeded the received precipitation frequency. As an example, the agricultural intelligence computer system may identify each location where the difference between the expected precipitation frequency and the received precipitation frequency is greater than 0.05, or 5%. Each identified location may be tagged as a persistent clutter location.

In an embodiment, persistent clutter locations are identified and/or updated periodically. For example, each month the agricultural intelligence computer system may identify persistent clutter locations based on the prior month's data. As another example, each day the persistent clutter locations may be identified based on the past 30 days of data. By identifying persistent clutter locations periodically, the agricultural intelligence computer system is able to capture seasonal variation in the persistent clutter.

3.5. Reducing Precipitation Values

At step 714, a maximum precipitation value for the particular location is generated using the plurality of forecast values. For example, the agricultural intelligence computer system may identify the maximum precipitation for each location and time as the maximum of the forecast values for that location and time from the various forecast sets at different lead times. Thus, if a forecast for 8:00 am with a lead time of 3 hours identifies a precipitation intensity of 2 mm and a forecast for 8:00 am with a lead time of 5 hours identifies a precipitation intensity of 3 mm, the agricultural intelligence computer system may select 3 mm as the maximum precipitation value. Thus, the agricultural intelligence computer system identifies the highest value ever forecasted for a specific location.

In an embodiment, the agricultural intelligence computer system additionally performs a spatial smoothing step with the maximum precipitation values in a similar manner as was performed for the probability of precipitation. For example, the agricultural intelligence computer system may initially use a simple average smoother with a radius of 5 grid points to smooth the maximum precipitation values across a grid for a particular time. The agricultural intelligence computer system may then identify each location where the smoothing step caused a reduction in the maximum precipitation value and replace the reduced value with the original maximum precipitation value for that location.

At step 716, the system determines that a particular digital data value representing precipitation value for the particular location is greater than the maximum precipitation value and, in response, replaces the particular digital data value with the maximum precipitation value. For example, the agricultural intelligence computer system may identify each location for a particular time that has been identified as a persistent clutter location and that has a received precipitation amount that is greater than the computed maximum precipitation value. For each identified location, the agricultural intelligence computer system may reduce the precipitation value to the computed maximum precipitation value.

4. Practical Applications

In an embodiment, the corrections to estimated precipitation are utilized as part of a practical application of managing an agronomic field. For example, the agricultural intelligence computer system may use the corrected precipitation values as inputs into digital models, such as a nutrient model as described in U.S. Pat. No. 9,519,861 or a yield model as described in US patent publication 2017-0169523A1, and the present disclosure is directed to persons who have knowledge and understanding of those patents. The models may be used to generate recommendations for agricultural management activities, such as changes in planting, fertility, weed control, irrigation, and/or harvesting of a crop.

In an embodiment, the agricultural intelligence computer uses the corrected prescription values to improve the usage of agricultural implements on the agronomic field. For example the agricultural intelligence computer system may generate one or more agricultural prescriptions based on the precipitation values and/or outputs of digital models. A prescription, as used herein, comprises a set of instructions for performing agricultural activities at a plurality of locations on an agronomic field. For example, a planting prescription may identify a crop to plant, a hybrid time, a seeding population, and/or one or more locations to plant the crop.

In an embodiment, the agricultural intelligence computer system additionally causes implementation of the prescription on an agronomic field by generating one or more scripts. The scripts comprise computer readable instructions which, when executed by an application controller, causes the application controller to control an operating parameter of an agricultural implement on the agronomic field to apply a treatment to the trial portion of the agronomic field. The scripts may be configured to match the generated prescription map such that the scripts, when executed, cause one or more agricultural implements to execute the prescriptions in the prescription map. The agricultural intelligence computer system may send the scripts to a field manager computing device and/or the application controller over a network.

In an embodiment, the agricultural intelligence computer system generates an improved display by including, in the display, the corrected prescription estimates to more accurately convey the precipitation history of a field. The display may comprise a map display which uses pixel values to indicate precipitation values for a plurality of locations. The map display may additionally indicate whether one or more agronomic fields managed by the field manager computing device received precipitation and, if so, at what intensity.

In an embodiment, instructions may be added to agricultural intelligence computer system 130 to utilize historical or adjusted precipitation rates to modify the way in which irrigation is applied at a field. For example instructions may be sent to agricultural intelligence computer system 130 comprising data relating to modified precipitation data to change the way in which the system operates irrigation field implements or recommend field treatment practices for irrigation based on the instructions.

5. Benefits of Certain Embodiments

When considered in light of the specification herein, and its character as a whole, this disclosure is directed to improvements in the use of radar sensors to measure precipitation as well as improvements in control of operations of field implements and equipment in agricultural, based on improvements in the radar sensor measurements. The disclosure is not intended to cover or claim the abstract model of forecasting weather but rather to the practical application of the use of computers to correct sensor equipment and to control agricultural machinery.

By correcting the sensor measurements, the system is additionally able to improve the accuracy, reliability, and usability of nutrient models and yield models which are utilized to determine agricultural activities. Thus, implementation of the invention described herein may have tangible benefits in increased agronomic yield of a crop, reduction in resource expenditure while managing a crop, and/or improvements in the crop itself.

6. Extensions and Alternatives

In the foregoing specification, embodiments have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the disclosure, and what is intended by the applicants to be the scope of the disclosure, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. 

1. A computer-implemented method providing improvements in agricultural science by improving sensor measurements of precipitation, the method comprising: receiving one or more digital precipitation records comprising a plurality of digital data values representing precipitation amounts at a plurality of locations; receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts at the plurality of locations; identifying, using the one or more digital precipitation records and the one or more digital forecast records, a plurality of forecast values for the plurality of locations; computing, based on the plurality of forecast values, a probability of precipitation at each of the plurality of locations; determining that the probability of precipitation at a particular location is lower than a threshold probability; and in response to determining that the probability of precipitation at a particular location is lower than the threshold probability, digitally storing data identifying the particular location as having received less precipitation than expected.
 2. The computer-implemented method of claim 1, further comprising: inputting the digitally stored data identifying the particular location as having received less precipitation than expected into a digital agricultural model; using the digital agricultural model, generating one or more recommendations for agricultural management activities at the plurality of locations.
 3. The computer-implemented method of claim 1, further comprising: using the digitally stored data identifying the particular location as having received less precipitation than expected, generating a set of instructions for performing agricultural activities by one or more agricultural implements at the plurality of locations; performing, by the one or more agricultural implements, based on the set of instructions, a treatment to a portion of an agronomic field.
 4. The computer-implemented method of claim 1, wherein: the plurality of locations represent a plurality of grid locations, the plurality of grid locations being part of an agronomic field represented by a grid; computing a probability of precipitation at each of the plurality of locations comprises computing a probability of precipitation at each of the plurality of grid locations in the grid; determining that the probability of precipitation at a particular location is lower than a threshold probability comprises determining that the probability of precipitation at a particular grid location is lower than a threshold probability; digitally storing data identifying the particular location as having received less precipitation than expected comprises digitally storing data identifying the particular grid location as having receive less precipitation than expected.
 5. The computer-implemented method of claim 4, further comprising: determining one or more erroneous grid locations having a probability of precipitation that is lower than the probability of precipitation at adjacent grid locations; modifying the probability of precipitation at the one or more erroneous grid locations by averaging the probability of precipitation of the adjacent grid locations for each of the one or more erroneous grid locations.
 6. The computer-implemented method of claim 1, wherein: each digital data value of the plurality of digital data values representing precipitation forecasts at the plurality of locations comprises estimations of precipitation at the plurality of locations at a plurality of lead times; each forecast value of the plurality of forecast values corresponds to a different lead time; computing a probability of precipitation at each of the plurality of locations comprises computing a probability of precipitation at each of the plurality of locations using a weighted estimate, the weighted estimate based on the plurality of lead times and the plurality of forecast values.
 7. The computer-implemented method of claim 1, wherein: receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts at the plurality of locations comprises receiving digital forecast records from a first forecast source and a second forecast source, the first forecast source being different than the second forecast source; identifying a plurality of forecast values for the plurality of locations comprises identifying a first plurality of forecast values for the plurality of locations based on the first forecast source and identifying a second plurality of forecast values for the plurality of locations based on the second forecast source; computing a probability of precipitation at each of the plurality of locations comprises computing a combined probability of precipitation based on the combination of a first weighted value and the first plurality of forecast values and a second weighted value and the second plurality of forecast values.
 8. A computer-implemented method providing improvements in agricultural science by improving sensor measurements of precipitation, the method comprising: receiving one or more digital precipitation records comprising a plurality of digital data values representing precipitation amounts at a plurality of locations; receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts at the plurality of locations; identifying, using the one or more digital precipitation records and the one or more digital forecast records, a plurality of forecast values for the plurality of locations; determining that a particular location of the plurality of locations is a persistent clutter location, wherein a persistent clutter location is a location associated with frequent false precipitation readings; based on determining that the particular location is a persistent clutter location, generating a maximum precipitation value for the particular location using the plurality of forecast values; determining that a particular digital data value representing a precipitation value for the particular location is greater than the maximum precipitation value; and in response to determining that a particular digital data value is greater than the maximum precipitation value, replacing the particular digital data value with the maximum precipitation value.
 9. The computer-implemented method of claim 8, further comprising: inputting one or more particular digital data values into a digital agricultural model, the one or more particular digital data values comprising digital data values that have been replaced with a maximum precipitation value; using the digital agricultural model, generating one or more recommendations for agricultural management activities at the plurality of locations.
 10. The computer-implemented method of claim 8, further comprising: using one or more particular digital data values, generating a set of instructions for performing agricultural activities by one or more agricultural implements at the plurality of locations, the one or more particular digital data values comprising digital data values that have been replaced with a maximum precipitation value; performing, by the one or more agricultural implements, based on the set of instructions, a treatment to a portion of an agronomic field.
 11. The computer-implemented method of claim 8, wherein: the plurality of locations represent a plurality of grid locations, the plurality of grid locations being part of an agronomic field represented by a grid; determining that a particular location of the plurality of locations is a persistent clutter location comprises determining that a particular grid location of the grid is a persistent clutter location; generating a maximum precipitation value for the particular location using the plurality of forecast values comprises generating a maximum precipitation value for the particular grid location; determining that a particular digital data value representing a precipitation value for the particular location is greater than the maximum precipitation value comprises determining that a particular digital data value representing a precipitation value for the particular grid location is great than the maximum precipitation value.
 12. The computer-implemented method of claim 11 wherein determining that a particular digital data value representing a precipitation value for the particular location is greater than the maximum precipitation value further comprises determining that the particular grid location is an erroneous grid location having a probability of precipitation that is greater than the probability of precipitation at adjacent grid locations.
 13. The computer-implemented method of claim 8, wherein: each digital data value of the plurality of digital data values representing precipitation forecasts at the plurality of locations comprises estimations of precipitation at the plurality of locations at a plurality of lead times; each forecast value of the plurality of forecast values corresponds to a different lead time; determining that a particular location of the plurality of locations is a persistent clutter location comprises using a weighted estimate, the weighted estimate based on the plurality of lead times and the plurality of forecast values.
 14. The computer-implemented method of claim 8, wherein: receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts at the plurality of locations comprises receiving digital forecast records from a first forecast source and a second forecast source, the first forecast source being different than the second forecast source; identifying a plurality of forecast values for the plurality of locations comprises identifying a first plurality of forecast values for the plurality of locations based on the first forecast source and identifying a second plurality of forecast values for the plurality of locations based on the second forecast source; determining that a particular location of the plurality of locations is a persistent clutter location is further based on the combination of a first weighted value and the first plurality of forecast values and a second weighted value and the second plurality of forecast values.
 15. An irrigation treatment system for providing improvements in agricultural science by optimizing irrigation treatment placements for testing in a field comprising a processor and main memory comprising instructions which, when executed, cause: receiving one or more digital precipitation records comprising a plurality of digital data values representing precipitation amounts at a plurality of locations; receiving one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts at the plurality of locations; identifying, using the one or more digital precipitation records and the one or more digital forecast records, a plurality of forecast values for the plurality of locations; computing, based on the plurality of forecast values, a probability of precipitation at each of the plurality of locations; determining that the probability of precipitation at a particular location is lower than a threshold probability; and in response to determining that the probability of precipitation at a particular location is lower than the threshold probability, digitally storing data identifying the particular location as having received less precipitation than expected.
 16. The irrigation treatment system of claim 15, the main memory further comprising instructions which, when executed, cause: inputting the digitally stored data identifying the particular location as having received less precipitation than expected into a digital agricultural model; using the digital agricultural model, generating one or more recommendations for agricultural management activities at the plurality of locations, the one or more recommendations comprising irrigation recommendations for the plurality of locations.
 17. The irrigation treatment system of claim 15, further comprising one or more irrigation agricultural implements operating at the plurality of locations and the main memory further comprising instructions which, when executed, cause: using the digitally stored data identifying the particular location as having received less precipitation than expected, generating a set of instructions for performing irrigational agricultural activities by the one or more irrigational agricultural implements at the plurality of locations; performing, by the one or more irrigational agricultural implements, based on the set of instructions, an irrigation treatment to a portion of an agronomic field.
 18. The irrigation treatment system of claim 15, wherein: the plurality of locations represent a plurality of grid locations, the plurality of grid locations being part of an agronomic field represented by a grid; computing a probability of precipitation at each of the plurality of locations comprise computing a probability of precipitation at each of the plurality of grid locations in the grid; determining that the probability of precipitation at a particular location is lower than a threshold probability comprises determining that the probability of precipitation at a particular grid location is lower than a threshold probability; digitally storing data identifying the particular location as having received less precipitation than expected comprises digitally storing data identifying the particular grid location as having receive less precipitation than expected.
 19. The irrigation treatment system of claim 18, the main memory further comprising instructions which, when executed, cause: determining one or more erroneous grid locations having a probability of precipitation that is lower than the probability of precipitation at adjacent grid locations; modifying the probability of precipitation at the one or more erroneous grid locations by averaging the probability of precipitation of the adjacent grid locations for each of the one or more erroneous grid locations.
 20. The irrigation treatment system of claim 15, wherein: each digital data value of the plurality of digital data values representing precipitation forecasts at the plurality of locations comprises estimation of precipitation at the plurality of locations at a plurality of lead times; each forecast value of the plurality of forecast values corresponds to a different lead time; computing a probability of precipitation at each of the plurality of locations comprises computing a probability of precipitation at each of the plurality of locations using a weighted estimate, the weighted estimate based on the plurality of lead times and the plurality of forecast values. 